Telecommunications infrastructure system and method

ABSTRACT

Exemplary implementations may: receive, by a sales support microservice in communication with a trained model running on a server, a plurality of attributes; and feed the plurality of attributes to the trained model.

TECHNICAL FIELD

Embodiments of the present disclosure generally relate to systems andmethods for automated fee generation, and more specifically to machinelearning based fee generation for telecommunications projects.

BACKGROUND

In the telecommunications industry, fee pricing for proposed projects istypically determined on a case-by-case and negotiated basis. In manycases, neither party to a proposed project has knowledge of similarproject pricing information. Further, where project pricing informationis compared across projects, it is generally through a manual and timeintensive process that is also error prone or limited to only a smallsubset of projects that may or may not in actuality be similar. As aresult, time and energy is spent on manually generating a baseline feeprice for a proposed project that is often renegotiated to a whollydifferent fee price. This greatly slows down the process of beginningprovisioning of projects and results in uncertainty during early phasesof project negotiation and highly variant fee pricing across projectsthat may otherwise be substantially similar to each other.

It is with these observations in mind, among others, that aspects of thepresent disclosure were concerned and developed.

SUMMARY

One aspect of the present disclosure relates to a system. The system mayinclude one or more hardware processors configured by machine-readableinstructions. The processor(s) may be configured to receive, by a salessupport microservice in communication with a trained model running on aserver, a plurality of attributes. The attributes may be descriptive ofa project related to a communications network. The processor(s) may beconfigured to feed the plurality of attributes to the trained model.Training the model may include retrieving historical data including acollection of tuples. Each tuple may include a final fee price forprovisioning a historical project and a plurality of historicalattributes. Training the model may include identifying historicalprojects which are similar to each other based on one of the final feeprice of each respective historical project or a portion of theplurality of historical attributes each respective historical project.Training the model may include refining a correlation between thehistorical attributes and the final fee price. The correlation ismappable, or able to be mapped or provide mappings, to the plurality ofattributes. Training the model may include generating, by the trainedmodel, a fee price for provisioning the project based on the pluralityof attributes.

Another aspect of the present disclosure relates to a method. The methodmay include receiving, by a sales support microservice in communicationwith a trained model running on a server, a plurality of attributes. Theattributes may be descriptive of a project related to a communicationsnetwork. The method may include feeding the plurality of attributes tothe trained model. Training the model may include retrieving historicaldata including a collection of tuples. Each tuple may include a finalfee price for provisioning a historical project and a plurality ofhistorical attributes. Training the model may include identifyinghistorical projects which are similar to each other based on one of thefinal fee price of each respective historical project or a portion ofthe plurality of historical attributes each respective historicalproject. Training the model may include refining a correlation betweenthe historical attributes and the final fee price. The correlation ismappable to the plurality of attributes. Training the model may includegenerating, by the trained model, a fee price for provisioning theproject based on the plurality of attributes.

Yet another aspect of the present disclosure relates to a non-transientcomputer-readable storage medium having instructions embodied thereon,the instructions being executable by one or more processors to perform amethod. The method may include receiving, by a sales supportmicroservice in communication with a trained model running on a server,a plurality of attributes. The attributes may be descriptive of aproject related to a communications network. The method may includefeeding the plurality of attributes to the trained model. Training themodel may include retrieving historical data including a collection oftuples. Each tuple may include a final fee price for provisioning ahistorical project and a plurality of historical attributes. Trainingthe model may include identifying historical projects which are similarto each other based on one of the final fee price of each respectivehistorical project or a portion of the plurality of historicalattributes each respective historical project. Training the model mayinclude refining a correlation between the historical attributes and thefinal fee price. The correlation is mappable to the plurality ofattributes. Training the model may include generating, by the trainedmodel, a fee price for provisioning the project based on the pluralityof attributes.

These and other features, and characteristics of the present technology,as well as the methods of operation and functions of the relatedelements of structure, will become more apparent upon consideration ofthe following description and the appended claims with reference to theaccompanying drawings, all of which form a part of this specification,wherein like reference numerals designate corresponding parts in thevarious figures. It is to be expressly understood, however, that thedrawings are for the purpose of illustration and description only andare not intended as a definition of the limits of the invention. As usedin the specification and in the claims, the singular form of ‘a’, ‘an’,and ‘the’ include plural referents unless the context clearly dictatesotherwise.

BRIEF DESCRIPTION OF THE DRAWINGS

The various features and advantages of the technology of the presentdisclosure will be apparent from the following description of particularembodiments of those technologies, as illustrated in the accompanyingdrawings. It should be noted that the drawings are not necessarily toscale; however the emphasis instead is being placed on illustrating theprinciples of the technological concepts. The drawings depict onlytypical embodiments of the present disclosure and, therefore, are not tobe considered limiting in scope.

FIG. 1 illustrates a system configured to automatically generate a feeprice for a project, in accordance with one or more implementations.

FIG. 2 illustrates a method for automatically generating a fee price fora project, in accordance with one or more implementations.

DETAILED DESCRIPTION

Historical project and negotiations data can be used to train a machinelearning model to generate base fee prices at the outset of negotiatinga telecommunications project fee price. For example, in deploying anenterprise network to a campus, various factors are taken intoconsideration in determining an initial price offered for taking on thetelecommunications project. Labor, materials, regulatory fees, and thelike factor into a price determination, as well as, for example andwithout imputing limitation, customer size, historical pricing forproject of a similar type, historical pricing for projects for the samecustomer, etc.

Further, the initial base fee used often directly impacts the finalsettled-upon pricing of the project. As a result, determining anaccurate initial base fee may be important in negotiating a project feeand so significant time and energy may be spent, prior to negotiating aproject fee, in generating the initial base fee.

Historical data may be used to train a machine learning model toefficiently and automatically generate a base fee price fortelecommunications projects. As a result, time and energy may be savedby utilizing the automatically generated base fee price. The trainedmachine learning model may use various types of data, from whichfeatures can be extracted and processed to predict a fee price based onlearned mappings of features to pricing biases, or estimates. Themappings are learned through model training processes (e.g., supervisedlearning, etc.).

Additionally, the same trained model, or a different trained model, canidentify historical projects that are similar to the project for which abase fee price is being generated. Project similarity may be determinedalong dimensions (e.g., total price value, location, initial fee priceto negotiated price difference, etc.) or along complex and/or non-humaninterpretable dimensions, such as those identified by, for example andwithout imputing limitation, a deep neural network or the like.Nevertheless, the identified similar historical projects can be providedfor further understanding of the various factors of determining andnegotiating a telecommunications project price.

FIG. 1 illustrates a system 100, in accordance with one or moreimplementations. In some implementations, system 100 may include one ormore servers 102. Server(s) 102 may be configured to communicate withone or more client computing platforms 104 according to a client/serverarchitecture and/or other architectures. Client computing platform(s)104 may be configured to communicate with other client computingplatforms via server(s) 102 and/or according to a peer-to-peerarchitecture and/or other architectures. Users may access system 100 viaclient computing platform(s) 104.

Server(s) 102 may be configured by machine-readable instructions 106.Machine-readable instructions 106 may include one or more instructionmodules. The instruction modules may include computer program modules.The instruction modules may include one or more of an attributereceiving module 108, a deployed model module 110, a display module 112,a usage recording module 114, a fee price generating module 116, aconfidence value generating module 118, and/or other instructionmodules.

Attribute receiving module 108 may be configured to, for example,receive a plurality of attributes to be provided to a sales supportmicroservice in communication with a trained model running on a server.In some examples, the sale support microservice is one microservice of aplurality of microservices (e.g., forming a service mesh) that supportsvarious information technology (IT) infrastructural processes andservices. Sales teams may interface with the sales support microservicevia an interface directly or through a third-party application offeringintegrations and the like.

The plurality of attributes may relate to various aspects of a projectfor installing, updating, modifying, or otherwise performing work on acommunications network for a prospective or current customer. Forexample, attributes related to the customer, geographically local (tothe customer) communications infrastructure and other environmentalaspects, and/or other items having to do with provisioning the project,such as internal project load and schedules for a provider of theproject, may be directly or indirectly among the plurality of attributesreceived by attribute receiving module 108. The attribute receivingmodule 108 can be communicatively linked to a client computing platform104 to receive the attributes as input via a user interface 105.

The attributes may be provided, or “fed,” to deployed model module 110by attribute receiving module 108. Deployed model module 110 may ingestthe attributes in order to classify the project associated with theattributes and/or generate a fee price for provisioning the project. Thefee price may be used to negotiate a deal regarding provisioning theproject. For example, the generated fee price can be provided to a salesclient for negotiating purposes or may be further processed viadownstream applications to provide an automated pricing service topurchasing clients via a website and the like. Deployed model module 110may include one or more models that have been trained to classifyprojects and/or predict a fee to charge for the projects. In someexamples, deployed model module 110 may include an ensemble model whichaggregates outputs from a plurality of different models forclassification and/or prediction purposes. Deployed model module 110 canretrieve the models from a model storage module 124, which may providemodel selection, storage, and additional training processes for modelsdeployed via deployed model module 110. Model storage module 124 maytrain models by accessing training and/or historical data via trainingand historical data module 126. Either or both of model storage module124 and training and historical data module 126 can store data (e.g.,historical data, training data, models, etc.) within electronic storage122.

By way of non-limiting example, the plurality of attributes may includeone or more of a customer channel, a vertical related to the prospectiveproject, a location of the project, a customer size, a product typeassociated with the project, or a service type associated with theproject. Additionally, the project may be part of a business-to-businesstransaction (e.g., provided to an enterprise customer such as in thecase of a workplace intranet, etc.) or a business-to-consumertransaction (e.g., provided to a consumer customer such as a homenetwork for an individual and the like).

Project display module 112 may be configured to display various outputsfrom modules 108, 110, 112, 114, 116, or 118. For example, projectdisplay module 112 may display (e.g., for a user, etc.) a classificationor a fee price produced by deployed model module 110. Project displaymodule 112 may also provide a visual prompt, in the form of a fillablefield and the like, for providing attributes according to attributereceiving module 108 parameters. In some examples, project displaymodule 112 can display historical projects similar to a projectdescribed by attributes provided to attribute receiving module 108. Thehistorical projects can be identified via additional trained models,which may be trained through unsupervised learning and the like,performing a clustering based analysis or similar analysis foridentifying similarities among large data sets. As a result, a user mayutilize the historical models as, for example, exemplars for timelines,project scoping, costs, support needs, and the like.

Usage recording module 114 may be configured to record a usage of thegenerated fee price, such as, for example, during a negotiation and/oralso as directly interacted with (e.g., clicked on, viewed, etc.) via auser interface (UI) 105. By way of non-limiting example, the usage mayinclude one of transacting at the generated fee price, transactingwithin a range of the generated fee price, or directly interacting withthe generated fee price (e.g., providing edits or modifications to thefee price, viewing the fee price, clicking the fee price, or otherinterface-based interactions that may be recorded through UI 105, etc.).In some examples, usage recording module 114 can record user behaviorinteracting with project display module 112 such as interface componentsselected, expanded, or interacted with, scrolling (e.g., via mousewheel) patterns, time spent on particular interface items, and variousother interactivities with a rendered user interface in order todetermine optimal interface configuration and/or data provided.

Fee price generating module 116 may be configured to generate, by thetrained model, a fee price range above or below the generated fee price.The fee price range may include additional fee prices at which theproject may be provisioned. In some examples, the fee price range may beequal on either side of the generated fee price (e.g., a generated feeprice with a +/− value indicating that any fee above or below thegenerated fee price of the +/− value has substantially similarqualities). The additional fee prices may include considerations such aslikelihood of being accepted by a potential client or customer, profitmargin, likelihood of being accurate (e.g., whether additional fees mayarise throughout provisioning the project, etc.), and the like.

In some examples, the fee price range may be determined by a confidencethreshold associated with the trained model of fee price generatingmodule 116. For example, confidence bounds, such as a 5% for example andwithout imputing limitation, may cause fee price generating module 116to include within the fee price range other fees falling within a 5%level of confidence (e.g., relative to confidence levels generated byconfidence value generating module 118 discussed below) of the generatedfee. In some other examples, a hard rule can be applied to generate feeswithin a predetermined monetary threshold of the generated fee price. Inyet other examples, variance of fee prices overall may be used todetermine the fee price range such as by determining a standarddeviation of all fees across projects or across a selection of projectsand including other fees prices in the fee price range that are within asingle standard deviation, or the like. Further, various combinationsand variations of the aforesaid examples may be used to generate the feeprice range.

Confidence value generating module 118 may be configured to generate oneor more confidence values associated with one of the generated fee priceor one or more values within the fee price range. Generally, aconfidence value will be within a range of 0 to 1.0, or 0% to 100%.Confidence values may indicate a confidence in a prediction that amodel, ensemble, or other fee generating process determines in agenerated fee being successful.

In some implementations, training the model may include retrievinghistorical data for contract projects including a final fee price andvarious other historical attributes. The data may be stored as acollection of tuples. In some implementations, each tuple may include afinal fee price for provisioning a historical project and a plurality ofhistorical attributes. Additionally, in some implementations, trainingthe model may include identifying historical projects which are similarto each other based on one of the final fee price of each respectivehistorical project or a portion of the plurality of historicalattributes each respective historical project and other forms ofclustering. Further, the tuples may be retrieved for rendering variousinterface information by, for example, project display module 112.

In some implementations, server(s) 102, client computing platform(s)104, and/or external resources 120 may be operatively linked via one ormore electronic communication links. For example, such electroniccommunication links may be established, at least in part, via a networksuch as the Internet and/or other networks. It will be appreciated thatthis is not intended to be limiting, and that the scope of thisdisclosure includes implementations in which server(s) 102, clientcomputing platform(s) 104, and/or external resources 120 may beoperatively linked via some other communication media.

A given client computing platform 104 may include one or more processorsconfigured to execute computer program modules. The computer programmodules may be configured to enable an expert or user associated withthe given client computing platform 104 to interface with system 100and/or external resources 120, and/or provide other functionalityattributed herein to client computing platform(s) 104. By way ofnon-limiting example, the given client computing platform 104 mayinclude one or more of a desktop computer, a laptop computer, a handheldcomputer, a tablet computing platform, a NetBook, a Smartphone, a gamingconsole, and/or other computing platforms.

External resources 120 may include sources of information outside ofsystem 100, external entities participating with system 100, and/orother resources. In some implementations, some or all of thefunctionality attributed herein to external resources 120 may beprovided by resources included in system 100.

Server(s) 102 may include electronic storage 122, one or more processors128, and/or other components. Server(s) 102 may include communicationlines, or ports to enable the exchange of information with a networkand/or other computing platforms. Illustration of server(s) 102 in FIG.1 is not intended to be limiting. Server(s) 102 may include a pluralityof hardware, software, and/or firmware components operating together toprovide the functionality attributed herein to server(s) 102. Forexample, server(s) 102 may be implemented by a cloud of computingplatforms operating together as server(s) 102.

Electronic storage 122 may comprise non-transitory storage media thatelectronically stores information. The electronic storage media ofelectronic storage 122 may include one or both of system storage that isprovided integrally (i.e., substantially non-removable) with server(s)102 and/or removable storage that is removably connectable to server(s)102 via, for example, a port (e.g., a USB port, a firewire port, etc.)or a drive (e.g., a disk drive, etc.). Electronic storage 122 mayinclude one or more of optically readable storage media (e.g., opticaldisks, etc.), magnetically readable storage media (e.g., magnetic tape,magnetic hard drive, floppy drive, etc.), electrical charge-basedstorage media (e.g., EEPROM, RAM, etc.), solid-state storage media(e.g., flash drive, etc.), and/or other electronically readable storagemedia. Electronic storage 122 may include one or more virtual storageresources (e.g., cloud storage, a virtual private network, and/or othervirtual storage resources). Electronic storage 122 may store softwarealgorithms, information determined by processor(s) 128, informationreceived from server(s) 102, information received from client computingplatform(s) 104, and/or other information that enables server(s) 102 tofunction as described herein.

Processor(s) 128 may be configured to provide information processingcapabilities in server(s) 102. As such, processor(s) 128 may include oneor more of a digital processor, an analog processor, a digital circuitdesigned to process information, an analog circuit designed to processinformation, a state machine, and/or other mechanisms for electronicallyprocessing information. Although processor(s) 128 is shown in FIG. 1 asa single entity, this is for illustrative purposes only. In someimplementations, processor(s) 128 may include a plurality of processingunits. These processing units may be physically located within the samedevice, or processor(s) 128 may represent processing functionality of aplurality of devices operating in coordination. Processor(s) 128 may beconfigured to execute modules 108, 110, 112, 114, 116, and/or 118,and/or other modules. Processor(s) 128 may be configured to executemodules 108, 110, 112, 114, 116, and/or 118, and/or other modules bysoftware; hardware; firmware; some combination of software, hardware,and/or firmware; and/or other mechanisms for configuring processingcapabilities on processor(s) 128. As used herein, the term “module” mayrefer to any component or set of components that perform thefunctionality attributed to the module. This may include one or morephysical processors during execution of processor readable instructions,the processor readable instructions, circuitry, hardware, storage media,or any other components.

It should be appreciated that although modules 108, 110, 112, 114, 116,and/or 118 are illustrated in FIG. 1 as being implemented within asingle processing unit, in implementations in which processor(s) 128includes multiple processing units, one or more of modules 108, 110,112, 114, 116, and/or 118 may be implemented remotely from the othermodules. The description of the functionality provided by the differentmodules 108, 110, 112, 114, 116, and/or 118 described below is forillustrative purposes, and is not intended to be limiting, as any ofmodules 108, 110, 112, 114, 116, and/or 118 may provide more or lessfunctionality than is described. For example, one or more of modules108, 110, 112, 114, 116, and/or 118 may be eliminated, and some or allof its functionality may be provided by other ones of modules 108, 110,112, 114, 116, and/or 118. As another example, processor(s) 128 may beconfigured to execute one or more additional modules that may performsome or all of the functionality attributed below to one of modules 108,110, 112, 114, 116, and/or 118.

FIG. 2 illustrates a method 200 for generating a fee price for aproject, in accordance with one or more implementations. The operationsof method 200 presented below are intended to be illustrative. In someimplementations, method 200 may be accomplished with one or moreadditional operations not described, and/or without one or more of theoperations discussed. Additionally, the order in which the operations ofmethod 200 are illustrated in FIG. 2 and described below is not intendedto be limiting.

In some implementations, method 200 may be implemented in one or moreprocessing devices (e.g., a digital processor, an analog processor, adigital circuit designed to process information, an analog circuitdesigned to process information, a state machine, and/or othermechanisms for electronically processing information). The one or moreprocessing devices may include one or more devices executing some or allof the operations of method 200 in response to instructions storedelectronically on an electronic storage medium. The one or moreprocessing devices may include one or more devices configured throughhardware, firmware, and/or software to be specifically designed forexecution of one or more of the operations of method 200. Further, whilemethod 200 is described below as deployed over a microservicesarchitecture, it is understood that monolithic, mixed-architecture, andvarious other architectures may be used to execute some or all of method200.

An operation 202 may include receiving, by a sales support microservicein communication with a trained model running on a server, a pluralityof attributes. The sales support microservice may be an instantiatedprogram within a service mesh accessible over a network (e.g., anenterprise network, virtual network, intranet, etc.) and provides accessto various functionality for sales and/or customer service agents. Theattributes may be descriptive of a project to be provisioned and theproject may be related to a communications network (e.g., intranetinstallation, internet connection, etc.). Operation 202 may be performedby one or more hardware processors configured by machine-readableinstructions including a module that is the same as or similar toattribute receiving module 108, in accordance with one or moreimplementations.

An operation 204 may include feeding the plurality of attributes to thetrained model. Operation 204 may be performed by one or more hardwareprocessors configured by machine-readable instructions including amodule that is the same as or similar to deployed model module 110and/or fee price generating module 116, in accordance with one or moreimplementations. The trained model, based on the ingested attributes,can classify the project and generate a fee price for the project. Insome examples, the generated fee price may be based on theclassification.

An operation 206 may include identifying historical projects that aresimilar to the classified project via clustering and the like. In someexamples, the trained model of operation 202 above may also provideidentification of similar historical projects. In some examples, aseparate trained model may be used to identify the similar historicalprojects based on clustering analysis and the plurality of attributes.Operation 206 may be performed by one or more hardware processorsconfigured by machine-readable instructions including a module that isthe same as or similar to deployed model module 110. In some examples, arange of fee prices based on the generated fee price may be generated.Additionally, a confidence value in the generated fee price or the rangeof fee prices may be generated by one or more hardware processorsconfigured by machine-readable instructions including a module that isthe same as or similar to confidence value generating module 118.

An operation 209 may include displaying the generated fee price and thesimilar historical projects. Operation 208 may be performed by one ormore hardware processors configured by machine-readable instructionsincluding a module that is the same as or similar to project displaymodel module 110, in accordance with one or more implementations. Insome examples, a user may interact with the displayed information (e.g.,via an interface, etc.) and the user interactions may be tracked andrecorded by one or more hardware processors configured bymachine-readable instructions including a module that is the same as orsimilar to usage recording module 114.

Although the present technology has been described in detail for thepurpose of illustration based on what is currently considered to be themost practical and preferred implementations, it is to be understoodthat such detail is solely for that purpose and that the technology isnot limited to the disclosed implementations, but, on the contrary, isintended to cover modifications and equivalent arrangements that arewithin the spirit and scope of the appended claims. For example, it isto be understood that the present technology contemplates that, to theextent possible, one or more features of any implementation can becombined with one or more features of any other implementation.

We claim:
 1. A system comprising: one or more hardware processorsconfigured by machine-readable instructions to: train a machine learningmodel, wherein training the machine learning model comprises: retrievinghistorical data comprising a final fee price for provisioning ahistorical project and one or more historical attributes; identifyinghistorical projects which are similar to each other based on one of thefinal fee price of each respective historical project or the one or morehistorical attributes each respective historical project; and refining acorrelation between the historical attributes and the final fee price,the correlation mappable to the plurality of attributes; receive, by amicroservice in communication with the trained machine learning model, aplurality of attributes, the plurality of attributes descriptive of atelecommunications project related to a communications network; feed theplurality of attributes to the trained machine learning model; andgenerate a fee price for provisioning the telecommunications projectbased on the plurality of attributes using the trained machine learningmodel.
 2. The system of claim 1, wherein the one or more hardwareprocessors are further configured by machine-readable instructions todisplay a one of the historical projects based on the received pluralityof attributes, the displayed one of the historical projects associatedwith one or more historical attributes that are significantly similar tothe received plurality of attributes.
 3. The system of claim 1, whereinthe one or more hardware processors are further configured bymachine-readable instructions to record a usage of the generated feeprice, the usage including one of transacting at the generated feeprice, transacting within a range of the generated fee price, ordirectly interacting with the generated fee price.
 4. The system ofclaim 1, wherein the one or more hardware processors are furtherconfigured by machine-readable instructions to generate, by the trainedmachine learning model, a fee price range above or below the generatedfee price, the fee price range including additional fee prices at whichthe telecommunications project may be provisioned.
 5. The system ofclaim 4, wherein the one or more hardware processors are furtherconfigured by machine-readable instructions to generate one or moreconfidence values associated with one of the generated fee price or oneor more values within the fee price range.
 6. The system of claim 1,wherein the plurality of attributes comprises one or more of a customerchannel, a vertical related to the prospective sale, a location of thetelecommunications project, a customer size, a product type associatedwith the telecommunications project, or a service type associated withthe project.
 7. The system of claim 1, wherein the project is abusiness-to-business transaction.
 8. A method comprising: training amachine learning model, wherein training the machine learning modelcomprises: retrieving historical data comprising a final fee price forprovisioning a historical project and one or more historical attributes;identifying historical projects which are similar to each other based onone of the final fee price of each respective historical project or theone or more historical attributes each respective historical project;and refining a correlation between the historical attributes and thefinal fee price, the correlation mappable to the plurality ofattributes; receiving, by a microservice in communication with thetrained machine learning model, a plurality of attributes, the pluralityof attributes descriptive of a telecommunications project related to acommunications network; feeding the plurality of attributes to thetrained machine learning model; and generating a fee price forprovisioning the telecommunications project based on the plurality ofattributes using the trained machine learning model.
 9. The method ofclaim 8, further comprising displaying a one of the historical projectsbased on the received plurality of attributes, the displayed one of thehistorical projects associated with one or more historical attributesthat are significantly similar to the received plurality of attributes.10. The method of claim 8, further comprising recording a usage of thegenerated fee price, the usage including one of transacting at thegenerated fee price, transacting within a range of the generated feeprice, or directly interacting with the generated fee price.
 11. Themethod of claim 8, further comprising generating, by the trained machinelearning model, a fee price range above or below the generated feeprice, the fee price range including additional fee prices at which thetelecommunications project may be provisioned.
 12. The method of claim11, further comprising generating one or more confidence valuesassociated with one of the generated fee price or one or more valueswithin the fee price range.
 13. The method of claim 8, wherein theplurality of attributes comprises one or more of a customer channel, avertical related to the prospective sale, a location of the project, acustomer size, a product type associated with the telecommunicationsproject, or a service type associated with the telecommunicationsproject.
 14. The method of claim 8, wherein the project is abusiness-to-business transaction.
 15. A non-transient computer-readablestorage medium having instructions embodied thereon, the instructionsbeing executable by one or more processors to perform a method, themethod comprising: training a machine learning model, wherein trainingthe machine learning model comprises: retrieving historical datacomprising a collection of tuples, each tuple including a final feeprice for provisioning a historical project and a plurality ofhistorical attributes; identifying historical projects which are similarto each other based on one of the final fee price of each respectivehistorical project or a portion of the plurality of historicalattributes each respective historical project; and refining acorrelation between the historical attributes and the final fee price,the correlation mappable to the plurality of attributes; receiving, by amicroservice in communication with the trained machine learning modelrunning on a server, a plurality of attributes, the plurality ofattributes descriptive of a telecommunications project related to acommunications network; feeding the plurality of attributes to thetrained machine learning model; and generate a fee price forprovisioning the telecommunications project based on the plurality ofattributes using the trained machine learning model.
 16. Thecomputer-readable storage medium of claim 15, wherein the method furthercomprises displaying a one of the historical projects based on thereceived plurality of attributes, the displayed one of the historicalprojects associated with a tuple of the collection of tuples, the tupleincluding a plurality of historical attributes that are significantlysimilar to the received plurality of attributes.
 17. Thecomputer-readable storage medium of claim 15, wherein the method furthercomprises recording a usage of the generated fee price, the usageincluding one of transacting at the generated fee price, transactingwithin a range of the generated fee price, or directly interacting withthe generated fee price.
 18. The computer-readable storage medium ofclaim 15, wherein the method further comprises generating, by thetrained machine learning model, a fee price range above or below thegenerated fee price, the fee price range including additional fee pricesat which the telecommunications project may be provisioned.
 19. Thecomputer-readable storage medium of claim 18, wherein the method furthercomprises generating one or more confidence values associated with oneof the generated fee price or one or more values within the fee pricerange.
 20. The computer-readable storage medium of claim 15, wherein theplurality of attributes comprises one or more of a customer channel, avertical related to the prospective sale, a location of thetelecommunications project, a customer size, a product type associatedwith the telecommunications project, or a service type associated withthe telecommunications project.